Relational Boosted Bandits
Keywords:Relational Probabilistic Models, Online Learning & Bandits
AbstractContextual bandits algorithms have become essential in real-world user interaction problems in recent years. However, these algorithms represent context as attribute value representation, which makes them infeasible for real world domains like social networks, which are inherently relational. We propose Relational Boosted Bandits (RB2), a contextual bandits algorithm for relational domains based on (relational) boosted trees. RB2 enables us to learn interpretable and explainable models due to the more descriptive nature of the relational representation. We empirically demonstrate the effectiveness and interpretability of RB2 on tasks such as link prediction, relational classification, and recommendation.
How to Cite
Kakadiya, A., Natarajan, S., & Ravindran, B. (2021). Relational Boosted Bandits. Proceedings of the AAAI Conference on Artificial Intelligence, 35(13), 12123-12130. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17439
AAAI Technical Track on Reasoning under Uncertainty